The wisdom of crowds can often provide better decision-making capabilities than the best guesses of experts. Automated systems have therefore evolved to offer predictive tools to institutional clients based upon the forecasts of a group of individuals; often implemented as software for sale to (or as a service for) institutional clients, a “prediction market” is typically relied upon to poll a group of individuals (i.e., “participants”) and use an averaged version of the group view to predict the outcome of one or more events. The “polling” can take various forms, such as permitting each participant to make a bet, trade a stock, provide an opinion, or engage in some other type of behavior or response that provides data that will serve as the basis for assessing group belief.
For example, a prediction market may be implemented by enterprise software within a large company, the software being used to poll a diverse group of executives as to a revenue forecast, or the real roll-out date of a product in development, or other type of “event.” The polling is often implemented in this context as either a game or virtual stock market, that is, a system that offers rewards or prizes to participants who make the right prediction; through the reward and continued participation, the prediction market seeks to elicit the participants' true, unbiased opinion as to the likely outcome of the event. Naturally, this example is not limiting, and many other examples of prediction markets exist—for example, a predication market can be used to accurately predict odds of a sporting event, such as a football game. Many different types of systems exist, with events being modeled as simple Boolean predictions (“I believe revenue this year will be greater than $1 Million US”), or using other forms of “virtual stocks” such as ranges (“I believe revenue this year will be between $1 Million US-$1.2 Million US”). Many other examples exist, and nearly any type of future event can be modeled using a prediction market. By aggregating together many such beliefs or forecasts, a prediction market typically seeks to compute the aggregate beliefs of a crowd, which if collected properly, are believed to be statistically more accurate than the belief of any one individual.
Generally speaking, prediction market systems are successful for their intended purposes, but often do have some limitations. For example, most systems permit forecasting of only the modeled event, that is, based on the actually collected views of all participants, and it may be difficult or impossible to segment the views of a particular class of participants, especially since the forecasts of that class may be dependent on the views of other classes. In addition, these systems typically can only predict results based on collected forecasts, that is, it may be impossible to understand what a specific group “might predict” presented with a hypothetical event for which no forecasts have yet been collected.
What is needed is a way to extract a measure of participant utility, that is, a measure of motivations and drives, in a prediction market. Such a solution would, generally speaking, facilitate more flexible prediction services and software, and additional applications of prediction market software. The present invention satisfies these needs and provides further related advantages.
The invention defined by the enumerated claims may be better understood by referring to the following detailed description, which should be read in conjunction with the accompanying drawings. This description of one or more particular embodiments, set out below to enable one to build and use various implementations of the invention or inventions set forth by the claims, is not intended to limit the enumerated claims, but to exemplify their application to certain methods and devices.
The description set out below exemplifies (i) a method of extracting utility of a participant or group of participants from a prediction market database that represents the views of a larger group, (ii) a prediction market system, implemented as a database that defines a prediction market through a series of closely-related databases (for example, that store records of participant predictions for one or more events, and that then models predictions for those events as a probability function based on the participant predictions), (iii) a series of related methods, including methods that aggregate participant inputs together to generate a cumulative probability function that can be used to model event likelihood, and (iv) related systems, methods and devices, including a prediction system as well as software stored on machine readable media that can be used to perform the aforementioned methods. The invention, however, may also be applied to other systems, methods and devices as well.